Layout Optimization of Multi-Type Sensors and Human Inspection Tools With Probabilistic Detection of Localized Damages for Pipelines

Abstract

In this paper, a new layout optimization approach for health monitoring of locally damaged and non-piggable segments of pipelines is presented. In contrast to the existing literature, the proposed optimization-based approach considers (i) sensors network and human inspection, as detection methods, together, (ii) severity level of damages, such as damage size and risk of failure, (iii) three probabilistic detection metrics (to detect, infer, and size different damages) and a health monitoring cost metric simultaneously, (iv) several key attributes of detection methods, such as data acquisition cost and frequency, in an optimization context, and (v) probabilistic sampling methods and probabilistic damage data. An optimization model for determining decision variables such as the type and location of sensors and human inspection areas and tools along a pipeline is formulated. Due to the unavailability of publicly available real-world data and benchmarks, applications of the proposed approach are demonstrated using two notional examples with synthetically generated damage data. In the first example, a step-by-step demonstration of the proposed approach is given. Considering the severity level of different localized damages, it is shown that the proposed layout optimization approach can obtain a better and more robust solution, especially when used during a pipeline design, compared to those that only consider a single detection method or rely on deterministic damage data. In the second example, a longer pipeline segment with a greater density of localized damages is considered to show applicability of the proposed approach to larger sized problems. Finally, based on the results obtained it is demonstrated that the proposed approach provides a practical solution for consideration of probabilistic detection metrics and probabilistic damage data corresponding to stochastic degradation processes in optimal health monitoring of pipelines.

System-level prognosis and health monitoring modeling framework and software implementation for gas pipeline system integrity management

“…The final step in the computation engine is the Sensor Placement Optimization module which is based on the integration of the results of the previous modules. It combines the bi-objective optimization model developed by Aria et al (Aria et al, 2020 with the previously described computation modules. The bi-objective function aims at maximizing the damage detection probability (DDP) at a minimal cost.…”

Experiments and modeling of fluctuating frictional force of pipeline inspection gauges

“…At present, PIGs that rely on pressure difference to provide travel power by using sealing discs to provide an interference seal are widely used [6][7][8]. When PIGs are working in a relatively closed working condition and the environment in the pipeline is complex [9,10], predicting the frictional force during its travel plays a very important role in determining the required pressure difference of front and rear, stable operating speed, and real-time position [11][12][13][14]. In addition, when PIG is traveling, the large self-excited vibration generated by the interference of the sealing disc boundary will affect the detection accuracy, and even cause structural failure and jamming problems [15][16][17][18].…”

Design and Development of an External Modular Pi-XTS Robot for Detecting Presence of Defects in Pipelines

“…Pipeline defects include cracks, gouges, corrosion, dents, and weld defects, which are classified as geometrical, planar discontinuity, and metal loss defects [3,4]. The inner and outer corrosion of the pipeline surface causes defects, that causes crack formation [5]. A leak can be defined as a failure criterion, but it can also be the result of poor equipment sealing [6].…”